Abstract
Radar antenna scan pattern (RASP) reconnaissance is a major problem in electronic warfare (EW). The RASP exerts a considerable influence on target identification, jamming decision making, and electronic support measures and thus plays a critical role in modern electronic warfare. A visibility graph (VG) is a tool for converting a time series into complex graphs with excellent noise immunity. This paper proposes a novel method for the intelligent recognition of the RASP based on the VG, including the circular, sector, helical, raster, conical, phased array, phased array azimuth and circular elevation scans. The changes in the signal amplitude received from the EW receiver are determined. Moreover, the related features are extracted from the VG and utilized to classify the RASPs. The comparison experiments performed with different classifiers, such as machine learning, neural network, and deep learning, confirm that the proposed method can improve the robustness of the recognition rate to the noise and recognition accuracy.
Highlights
The antenna scan type (AST) is a dominant parameter in the discrimination and solution of the ambiguities for the classification of radar threats
EXPERIMENTAL RESULTS The section describes the performance of the visibility graph (VG) algorithm for the Radar antenna scan pattern (RASP)
The properties of the VG algorithm are analyzed using a set of Monte Carlo simulations, and the VG algorithm is combined with the classification algorithms for different SNRs
Summary
The antenna scan type (AST) is a dominant parameter in the discrimination and solution of the ambiguities for the classification of radar threats. The purpose and working state of radars are different, leading to different antenna beam shapes and antenna scan patterns (ASPs). The ASP is key to identify the type and working state of the radar. The research on the ASP can help in the analysis and identification of the threat target signals in complex electromagnetic environments in the future, thereby further strengthening the military. Such intelligent technology can improve the quality of work management
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